1. Field
One or more embodiments of the present invention relate to a motion detection system, and more particularly, to a method, medium, and apparatus with estimation of a background change, such as included in a motion detection system, using a background model.
2. Description of the Related Art
Recently, as we become more aware of new threats such as terrorism or crimes by inner staff, visual surveillance and security systems are gradually being introduced within society, outside of their previous limited applications within non-civilian military or law enforcement facilities. In addition, the need for automation of intrusion detection using video cameras is also increasing due to expansions of monitoring areas, increased labor costs of monitoring staff, and attention deficits and distractions affecting monitoring staff, as proven through psychological experiments. Accordingly, visual-based intrusion detection products detecting motion, a widely known basic and important cue for detecting intrusion, have started to be introduced.
Motion detection techniques are generally classified into 3 groups, that is, a background subtraction-based motion detection technique using a statistical background model, a temporal difference-based motion detection technique, and an optic flow-based motion detection technique.
These three motion detection techniques have their own advantages and disadvantages. However, it is known that, when various characteristics are taken into consideration, such a background subtraction-based motion detection technique using a mixture of Gaussian models is more robust to noise and, in general, has a better detection performance than the other techniques. Hence, the background subtraction-based motion detection technique is widely used in intrusion detection systems.
The background subtraction-based motion detection technique is based on the assumption that a background remains unchanged for a long time when it is photographed by a fixed camera. Therefore, if the entire background changes due to the shaking of a camera, a motion detector applying the technique falsely recognizes a change in background as movements in the foreground. Accordingly, the conventional background subtraction-based motion detection technique cannot accurately detect a real moving object until the entire background has been relearned. That is, to recover from this malfunction, or mischaracterization, the motion detector needs to reset the prior background model and reorganize the model by relearning the background as soon as possible. In this case, a corresponding learning rate greatly affects the recovery time. For example, when the learning rate is set low, the motion detector cannot cope with periodic vibrations of a camera caused by intermittent strong winds, for example, and thereby can be stuck in a vicious cycle of endless relearning. If the learning rate is set high, these problems may be eased, but an object moving slowly in the foreground would not be distinguished as the foreground, but falsely learned to be a part of the background.
These disturbances may not only be caused by strong winds, but also by other factors such as trains running on railroads and subways, or cargo trucks on a nearby highway, which can be frequently found in densely populated urban areas. Therefore, although the conventional statistical background subtraction-based motion detection technique has, by its nature, a superior detection performance, the technique is not that reliable in many outdoor applications, especially in urban areas, because the increased frequency in false alarms deteriorates the value of automated intrusion detection.